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This is the 2.5 dB factor that we accounted for in the previous preamplifier
discussion, whenever the noise power out of the preamplifier was
approximately equal to or greater than the analyzer’s own noise.
Another factor that affects noise measurements is the bandwidth in which
the measurement is made. We have seen how changing resolution bandwidth
affects the displayed level of the analyzer’s internally generated noise.
Bandwidth affects external noise signals in the same way. To compare
measurements made on different analyzers, we must know the bandwidths
used in each case.
Not only does the 3 dB (or 6 dB) bandwidth of the analyzer affect the
measured noise level, the shape of the resolution filter also plays a role.
To make comparisons possible, we define a standard noise-power bandwidth:
the width of a rectangular filter that passes the same noise power as our
analyzer’s filter. For the near-Gaussian filters in Agilent analyzers, the
equivalent noise-power bandwidth is about 1.05 to 1.13 times the 3 dB
bandwidth, depending on bandwidth selectivity. For example, a 10 kHz
resolution bandwidth filter has a noise-power bandwidth in the range of
10.5 to 11.3 kHz.
If we use 10 log(BW
2
/BW
1
) to adjust the displayed noise level to what we
would have measured in a noise-power bandwidth of the same numeric value
as our 3 dB bandwidth, we find that the adjustment varies from:
10 log(10,000/10,500) = –0.21 dB
to
10 log(10,000/11,300) = –0.53 dB
In other words, if we subtract something between 0.21 and 0.53 dB from the
indicated noise level, we shall have the noise level in a noise-power bandwidth
that is convenient for computations. For the following examples below, we
will use 0.5 dB as a reasonable compromise for the bandwidth correction
9
.
Figure 5-7. The envelope of band-limited Gaussian noise has a Rayleigh distribution
9. ESA Series analyzers calibrate each RBW during
the IF alignment routine to determine the noise
power bandwidth. The PSA Series analyzers
specify noise power bandwidth accuracy to within
1% (±0.044 dB).
- Spectrum Analysis Basics 1
- Table of Contents 2
- — continued 3
- Chapter 1 4
- Introduction 4
- What is a spectrum? 5
- Why measure spectra? 6
- Types of measurements 8
- Types of signal analyzers 8
- Chapter 2 10
- Spectrum Analyzer 10
- Fundamentals 10
- Accuracy.” 11
- RF attenuator 12
- Tuning the analyzer 12
- IF gain 16
- Resolving signals 16
- H(4000) = –10(4) log 18
- Residual FM 19
- Phase noise 20
- Sweep time 22
- Digital resolution filters 23
- Envelope detector 24
- Displays 25
- Detector types 26
- Positive peak 27
- Negative peak 27
- One bucket 27
- (positive) detection 28
- Peak (positive) detection 29
- Negative peak detection 29
- Normal detection 29
- 1Buckets 2 3 4 5678910 31
- Average detection 32
- Averaging processes 33
- Video filtering 34
- Trace Averaging 36
- Time gating 38
- RF signal 40
- Gate signal 40
- Gated FFT 42
- Gated video 42
- Gated sweep 43
- Chapter 3 44
- Digital IF Overview 44
- The all-digital IF 45
- Custom signal processing IC 47
- Frequency counting 47
- Chapter 4 49
- Amplitude and 49
- Frequency Accuracy 49
- Frequency response 51
- Relative uncertainty 52
- Absolute amplitude accuracy 52
- Improving overall uncertainty 53
- The digital IF section 54
- Examples 55
- Frequency accuracy 56
- Chapter 5 58
- Sensitivity and Noise 58
- Noise figure 61
- Preamplifiers 62
- Spectrum analyzer 63
- –10 –5 0 +5 +10 64
- Noise as a signal 65
- 1% (±0.044 dB) 67
- Chapter 6 70
- Dynamic Range 70
- Attenuator test 74
- 2nd order 78
- Gain compression 80
- Chapter 7 83
- Extending the 83
- Frequency Range 83
- Image frequencies 86
- 4.7 4.9 5.1 5.3 88
- Preselector 90
- Amplitude calibration 91
- Improved dynamic range 92
- External harmonic mixing 96
- Signal identification 98
- Figure 7-17a. 14 100
- Chapter 8 102
- Modern Spectrum Analyzers 102
- Figure 8-1. CCDF measurement 103
- Digital modulation analysis 105
- Saving and printing data 106
- Firmware updates 108
- Glossary of Terms 110
- Agilent Email Updates 120
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